Dissecting FLOPs along input dimensions for GreenAI cost estimations

TL;DR

This paper refines FLOPs calculation for convolutional layers to better estimate energy consumption in GreenAI, accounting for input dimension effects and hardware parallelism discrepancies.

Contribution

It introduces { extalpha}-FLOPs, a new formula that improves FLOPs accuracy by considering input dimensions and non-uniform parallelism effects.

Findings

{ extalpha}-FLOPs better correlates with energy consumption.

Refined FLOPs formula accounts for input dimension effects.

Improves GreenAI cost estimations.

Abstract

The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods. The promoters of GreenAI suggested the use of Floating Point Operations (FLOPs) as a measure of the computational cost of Neural Networks; however, that measure does not correlate well with the energy consumption of hardware equipped with massively parallel processing units like GPUs or TPUs. In this article, we propose a simple refinement of the formula used to compute floating point operations for convolutional layers, called {\alpha}-FLOPs, explaining and correcting the traditional discrepancy with respect to different layers, and closer to reality. The notion of {\alpha}-FLOPs relies on the crucial insight that, in case of inputs with multiple dimensions, there is no reason to believe that the speedup offered by parallelism will be uniform along all different axes.